An Ontology Model for Smart Service in Vertical Farms – An OWL-S Approach

Sivamani, Saraswathi; Kim, Hong-Geun; Lee, Myeongbae; Park, Jang-Woo; Shin, Chang-Sun; Cho, Yongyun

doi:10.14257/ijunesst.2016.9.1.18

Cited by 4 publications

(5 citation statements)

References 9 publications

(5 reference statements)

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“…Their research concentrates more on the monitoring and controlling of the internal and external environment parameters. This work was extended to link VFO with web services aiming to assist different entities related to hardware, user, service, and environmental factors [48]. In the study by Kim et al, an ontology using context modeling technique is designed for an intelligent service in a vertical farm by integrating several environmental and control factors, which were validated for different scenarios [49].…”

Section: Agricultural Ontologiesmentioning

confidence: 99%

An ontology model to represent aquaponics 4.0 system’s knowledge

Abbasi

Martı́nez

Ahmad

2022

Information Processing in Agriculture

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Section: Agricultural Ontologiesmentioning

confidence: 99%

An ontology model to represent aquaponics 4.0 system’s knowledge

Abbasi

Martı́nez

Ahmad

2022

Information Processing in Agriculture

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“…al. [8] extended their work on vertical farming ontologies aided by web services through OWL-S approach which integrates various web services. OWL-S consists of three subclasses (i.e.…”

Section: Related Workmentioning

confidence: 99%

An Ontology-Driven Decision Support System for Rice Crop Production

Afzal

Kasi

et al. 2021

Journal of Applied and Emerging Sciences

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Agriculture domain now extensively uses the Internet of Things (IoTs) technology to provide farmers with proper and accurate information. Assisting farmers regularly and periodically in a more efficient manner is totally based on complete data, proper planning, and decision making. Connecting devices with each other through IoT has brought huge changes to traditional way of farming. However, it has also invited some challenges such as the semantic interoperability, quality and accuracy of data.In this paper, we extend a base farming ontology to include classes comprising of water, pesticides, and seeds information that is organized both seasonally and phase-wise. We have extended a farming ontology specifically a crop production domain using rice crop as a case study. Semantic Web Rule Language (SWRL) integrated with Jess rule engine is used for reasoning and inferencing to make devices understandable to each other. A collection of 54 SWRL rules reason about 101 OWL classes in order to maintain water irrigation in rice crops. It also provides pesticide and weedicide information for each growth stage along with seed information by identifying specific crop type. This helps the farmers to obtain better results in terms of production and sustainability from the collected data by offering them decision making support in the management of rice crops.

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“…The vocabulary of the CSA domain was constructed by reading related literature on CSA from websites ("Climate-Smart Agriculture", n.d.; Papuso & Faraby, 2013;FAO, 2017), published research articles and reports (Smith & Gregory, 2012;Lipper et al, 2014;Amin et al, 2015;FISH, 2016;FAO, 2018a;Raza et al, 2019) and analyzing existing related agricultural ontologies (Hu et al, 2011;Kim et al, 2013;Dineshkumar et al, 2014;Harika & Bhargav, 2015;Sivamani et al, 2016;Ngo et al, 2018). Furthermore, lists of competency questions (CQs) and role players involved in CSA were drawn to delimit the scope of the target OntoCSA ontology.…”

Section: Building the Vocabulary Of The Csa Domainmentioning

confidence: 99%

“…This is witnessed in recent reviews of the use of semantic technologies in agriculture (Jebaraj & Sathiaseelan, 2017;Drury et al, 2019) and a recent review and evaluation of existing agricultural ontologies (Goldstein et al, 2019). Many studies have specifically reported on the use of ontologies in agriculture (Hu et al, 2011;Kim et al, 2013;Dineshkumar et al, 2014;Harika & Bhargav, 2015;Sivamani et al, 2016;Jebaraj & Sathiaseelan, 2017;Ngo et al, 2018;Goldstein et al, 2019;Nidhi et al, 2021;Jachimczyk et al, 2021) and climate change (Diop & Lo, 2013;Pileggi & Lamia, 2020) domains. Kim et al (2013), Harika and Bhargav (2015) and Sivamani et al (2016) developed context aware ontologies for seamless services access in vertical farming.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies have specifically reported on the use of ontologies in agriculture (Hu et al, 2011;Kim et al, 2013;Dineshkumar et al, 2014;Harika & Bhargav, 2015;Sivamani et al, 2016;Jebaraj & Sathiaseelan, 2017;Ngo et al, 2018;Goldstein et al, 2019;Nidhi et al, 2021;Jachimczyk et al, 2021) and climate change (Diop & Lo, 2013;Pileggi & Lamia, 2020) domains. Kim et al (2013), Harika and Bhargav (2015) and Sivamani et al (2016) developed context aware ontologies for seamless services access in vertical farming. The proposed ontologies model the vertical farm environment and the factors that influence crop growth in such an environment.…”

Section: Literature Reviewmentioning

confidence: 99%

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OntoCSA

Fonou-Dombeu

Naidoo

Ramnanan

et al. 2021

International Journal of Agricultural and Environmental Information Systems

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The modelling of agriculture with ontologies has been of interest to many authors in the past years. However, no research, currently, has focused on building a knowledge base ontology for the Climate Smart Agriculture (CSA) domain. This study attempts to fill this gap through the development of a Climate Smart Agriculture Ontology (OntoCSA). Information was gathered from secondary sources including websites, published research articles and reports as well as related ontologies, to formalize the OntoCSA ontology in Description Logics (DLs). The OntoCSA ontology was developed in Web Ontology Language (OWL) with Protégé. Furthermore, the OntoCSA ontology was successfully validated with the HermiT reasoner within Protégé. The resulting OntoCSA ontology is a machine-readable model of CSA that can be leveraged in web-based applications for the storage, open and automated access and sharing of CSA information/data, for research and dissemination of best practices

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An Ontology Model for Smart Service in Vertical Farms – An OWL-S Approach

Cited by 4 publications

References 9 publications

An ontology model to represent aquaponics 4.0 system’s knowledge

An ontology model to represent aquaponics 4.0 system’s knowledge

An Ontology-Driven Decision Support System for Rice Crop Production

OntoCSA

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